{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eZPpsA4Sctc-"
      },
      "source": [
        "# **LLMs in 4 acts with Luis Roque**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p3_5xKQXdA4o"
      },
      "source": [
        "# **Act 0:** 依赖关系和环境设置"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "spA6jI9j8gjj",
        "outputId": "998b872b-6425-421b-8280-8507ee6aa321"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tmAWyJfZjRmA",
        "outputId": "3a293a89-e567-42d5-9d08-500bac4bb0fc"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
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            "Collecting deprecated>=1.2.6 (from opentelemetry-api>=1.2.0->chromadb)\n",
            "  Downloading Deprecated-1.2.14-py2.py3-none-any.whl (9.6 kB)\n",
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            "\u001b[?25hCollecting opentelemetry-instrumentation-asgi==0.42b0 (from opentelemetry-instrumentation-fastapi>=0.41b0->chromadb)\n",
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            "Building wheels for collected packages: pypika\n",
            "  Building wheel for pypika (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pypika: filename=PyPika-0.48.9-py2.py3-none-any.whl size=53723 sha256=87c493e15d71f8af80f7ce62699489f1547ae450477da3b01fcdfaa48d8bb099\n",
            "  Stored in directory: /root/.cache/pip/wheels/e1/26/51/d0bffb3d2fd82256676d7ad3003faea3bd6dddc9577af665f4\n",
            "Successfully built pypika\n",
            "Installing collected packages: pypika, monotonic, mmh3, websockets, uvloop, urllib3, typing-extensions, python-dotenv, pulsar-client, overrides, opentelemetry-util-http, opentelemetry-semantic-conventions, opentelemetry-proto, humanfriendly, httptools, deprecated, chroma-hnswlib, bcrypt, watchfiles, uvicorn, starlette, opentelemetry-exporter-otlp-proto-common, opentelemetry-api, coloredlogs, asgiref, posthog, opentelemetry-sdk, opentelemetry-instrumentation, onnxruntime, fastapi, opentelemetry-instrumentation-asgi, opentelemetry-exporter-otlp-proto-grpc, kubernetes, opentelemetry-instrumentation-fastapi, chromadb\n",
            "  Attempting uninstall: urllib3\n",
            "    Found existing installation: urllib3 2.0.7\n",
            "    Uninstalling urllib3-2.0.7:\n",
            "      Successfully uninstalled urllib3-2.0.7\n",
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            "    Found existing installation: typing_extensions 4.5.0\n",
            "    Uninstalling typing_extensions-4.5.0:\n",
            "      Successfully uninstalled typing_extensions-4.5.0\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "lida 0.0.10 requires kaleido, which is not installed.\n",
            "lida 0.0.10 requires python-multipart, which is not installed.\n",
            "tensorflow-probability 0.22.0 requires typing-extensions<4.6.0, but you have typing-extensions 4.8.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed asgiref-3.7.2 bcrypt-4.0.1 chroma-hnswlib-0.7.3 chromadb-0.4.18 coloredlogs-15.0.1 deprecated-1.2.14 fastapi-0.104.1 httptools-0.6.1 humanfriendly-10.0 kubernetes-28.1.0 mmh3-4.0.1 monotonic-1.6 onnxruntime-1.16.3 opentelemetry-api-1.21.0 opentelemetry-exporter-otlp-proto-common-1.21.0 opentelemetry-exporter-otlp-proto-grpc-1.21.0 opentelemetry-instrumentation-0.42b0 opentelemetry-instrumentation-asgi-0.42b0 opentelemetry-instrumentation-fastapi-0.42b0 opentelemetry-proto-1.21.0 opentelemetry-sdk-1.21.0 opentelemetry-semantic-conventions-0.42b0 opentelemetry-util-http-0.42b0 overrides-7.4.0 posthog-3.0.2 pulsar-client-3.3.0 pypika-0.48.9 python-dotenv-1.0.0 starlette-0.27.0 typing-extensions-4.8.0 urllib3-1.26.18 uvicorn-0.24.0.post1 uvloop-0.19.0 watchfiles-0.21.0 websockets-12.0\n",
            "Collecting datasets\n",
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            "Installing collected packages: pyarrow-hotfix, dill, multiprocess, datasets\n",
            "Successfully installed datasets-2.15.0 dill-0.3.7 multiprocess-0.70.15 pyarrow-hotfix-0.6\n"
          ]
        }
      ],
      "source": [
        "!pip install spotipy\n",
        "!pip install langchain\n",
        "!pip install openai\n",
        "!pip install transformers\n",
        "!pip install sentence-transformers\n",
        "!pip install faiss-cpu\n",
        "!pip install unstructured\n",
        "!pip install chromadb\n",
        "!pip install datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "VMvDYlCFCInk"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import json\n",
        "import time\n",
        "from typing import Dict, List, Any, Tuple\n",
        "import spotipy\n",
        "from spotipy.oauth2 import SpotifyClientCredentials\n",
        "from langchain.document_loaders import DirectoryLoader\n",
        "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
        "from langchain.embeddings import SentenceTransformerEmbeddings\n",
        "from langchain.vectorstores import Chroma\n",
        "from langchain.chat_models import ChatOpenAI\n",
        "from langchain.chains.question_answering import load_qa_chain\n",
        "from transformers import GPT2Tokenizer\n",
        "import openai\n",
        "from sentence_transformers import SentenceTransformer\n",
        "import faiss\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import random\n",
        "import pickle\n",
        "import os\n",
        "from transformers import (\n",
        "    AutoModelForCausalLM,\n",
        "    AutoTokenizer,\n",
        "    TrainingArguments,\n",
        "    Trainer,\n",
        ")\n",
        "from datasets import load_dataset\n",
        "\n",
        "random.seed(42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "bK5fssOBj6db"
      },
      "outputs": [],
      "source": [
        "work_path = \"/content/drive/MyDrive/Speaker/DSPT23_LLMsUnfolded\"\n",
        "env_file_path = os.path.join(work_path, '.env/env_variables.pkl')\n",
        "\n",
        "def load_secrets(file_path):\n",
        "    \"\"\"\n",
        "    Load secrets from a pickle file.\n",
        "\n",
        "    Args:\n",
        "        file_path (str): Path to the pickle file from which secrets will be loaded.\n",
        "\n",
        "    Returns:\n",
        "        dict: A dictionary containing the loaded secrets.\n",
        "    \"\"\"\n",
        "    if os.path.exists(file_path):\n",
        "        with open(file_path, 'rb') as file:\n",
        "            return pickle.load(file)\n",
        "    return {}\n",
        "\n",
        "# Example secrets\n",
        "secrets = {\n",
        "    'client_id': 'a088f7',\n",
        "    'client_secret': '193b350fd',\n",
        "    'OPENAI_API_KEY': 's8oY4QHhhW',\n",
        "    'HF_TOKEN': 'hCGdYekDcZLJi'\n",
        "}\n",
        "\n",
        "secrets = load_secrets(env_file_path)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oP3qETNbCacj"
      },
      "source": [
        "# **Act 1:** 摘要、人工智能功能和 ChatGPT 封装器"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nQlHMAkG8F3U"
      },
      "source": [
        "![ChatGPT Wrapper Image](https://drive.google.com/uc?export=view&id=1Gf9mlEOfcjwR2dwBgMTWHt7AmsMDCYH0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "LnilymFdijaV"
      },
      "outputs": [],
      "source": [
        "credentials_manager = SpotifyClientCredentials(client_id=secrets['client_id'], client_secret=secrets['client_secret'])\n",
        "sp = spotipy.Spotify(client_credentials_manager=credentials_manager)\n",
        "\n",
        "\n",
        "def get_new_releases(limit: int = 50, offset: int = 0) -> List[Dict[str, Any]]:\n",
        "    \"\"\"\n",
        "    Fetch new releases from Spotify.\n",
        "\n",
        "    Args:\n",
        "        limit (int, optional): Maximum number of album results to return. Defaults to 50.\n",
        "        offset (int, optional): The index of the first result to return. Defaults to 0.\n",
        "\n",
        "    Returns:\n",
        "        List[Dict[str, Any]]: A list of dictionaries containing album information.\n",
        "    \"\"\"\n",
        "    new_releases = sp.new_releases(limit=limit, offset=offset)\n",
        "    albums = new_releases[\"albums\"][\"items\"]\n",
        "    return albums\n",
        "\n",
        "\n",
        "def get_album_tracks(album_id: str) -> List[Dict[str, Any]]:\n",
        "    \"\"\"\n",
        "    Fetch tracks from a specific album.\n",
        "\n",
        "    Args:\n",
        "        album_id (str): The Spotify ID of the album.\n",
        "\n",
        "    Returns:\n",
        "        List[Dict[str, Any]]: A list of dictionaries containing track information.\n",
        "    \"\"\"\n",
        "    tracks = sp.album_tracks(album_id)[\"items\"]\n",
        "    return tracks\n",
        "\n",
        "\n",
        "def save_data_to_file(data: List[Dict[str, Any]], file_path: str) -> None:\n",
        "    \"\"\"\n",
        "    Save data to a JSON file.\n",
        "\n",
        "    Args:\n",
        "        data (List[Dict[str, Any]]): List of dictionaries containing album and track information.\n",
        "        file_path (str): Path to the JSON file where the data will be saved.\n",
        "    \"\"\"\n",
        "    with open(file_path, \"w\", encoding=\"utf-8\") as file:\n",
        "        json.dump(data, file, ensure_ascii=False, indent=4)\n",
        "\n",
        "\n",
        "def load_data_from_file(file_path: str) -> List[Dict[str, Any]]:\n",
        "    \"\"\"\n",
        "    Load data from a JSON file.\n",
        "\n",
        "    Args:\n",
        "        file_path (str): Path to the JSON file where the data is stored.\n",
        "\n",
        "    Returns:\n",
        "        List[Dict[str, Any]]: List of dictionaries containing album and track information.\n",
        "    \"\"\"\n",
        "    with open(file_path, \"r\", encoding=\"utf-8\") as file:\n",
        "        return json.load(file)\n",
        "\n",
        "\n",
        "def download_latest_albums_data() -> None:\n",
        "    \"\"\"\n",
        "    Download the latest albums and tracks data from Spotify and save it to a JSON file.\n",
        "    \"\"\"\n",
        "    limit = 50\n",
        "    offset = 0\n",
        "    total_albums = 20\n",
        "    album_count = 0\n",
        "\n",
        "    all_albums = []\n",
        "\n",
        "    while total_albums is None or album_count < total_albums:\n",
        "        new_releases = get_new_releases(limit, offset)\n",
        "        if total_albums is None:\n",
        "            total_albums = sp.new_releases()[\"albums\"][\"total\"]\n",
        "\n",
        "        for album in new_releases:\n",
        "            album_info = {\n",
        "                \"album_name\": album[\"name\"],\n",
        "                \"artist_name\": album[\"artists\"][0][\"name\"],\n",
        "                \"album_type\": album[\"album_type\"],\n",
        "                \"release_date\": album[\"release_date\"],\n",
        "                \"tracks\": [],\n",
        "            }\n",
        "\n",
        "            tracks = get_album_tracks(album[\"id\"])\n",
        "\n",
        "            for track in tracks:\n",
        "                track_info = {\n",
        "                    \"track_name\": track[\"name\"],\n",
        "                    \"duration_ms\": track[\"duration_ms\"],\n",
        "                }\n",
        "                album_info[\"tracks\"].append(track_info)\n",
        "\n",
        "            all_albums.append(album_info)\n",
        "            album_count += 1\n",
        "\n",
        "        offset += limit\n",
        "        time.sleep(1)  # Add a delay to avoid hitting the rate limit\n",
        "        print(f\"Downloaded {album_count}/{total_albums}\")\n",
        "\n",
        "    save_data_to_file(all_albums, \"albums_and_tracks.json\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "VB51yXj7jHu6"
      },
      "outputs": [],
      "source": [
        "data = load_data_from_file(os.path.join(work_path, 'albums_and_tracks.json'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TO-9osDIl7n8",
        "outputId": "ee510f44-755b-41ad-cb5c-98a9f7fdc21c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[{'album_name': 'Welcome 2 Collegrove',\n",
              "  'artist_name': '2 Chainz',\n",
              "  'album_type': 'album',\n",
              "  'release_date': '2023-11-17',\n",
              "  'tracks': [{'track_name': 'Scene 1: Welcome 2 Collegrove',\n",
              "    'duration_ms': 49154},\n",
              "   {'track_name': 'G6', 'duration_ms': 184572},\n",
              "   {'track_name': 'Big Diamonds (feat. 21 Savage)', 'duration_ms': 199597},\n",
              "   {'track_name': 'Presha', 'duration_ms': 185058},\n",
              "   {'track_name': 'Long Story Short', 'duration_ms': 210338},\n",
              "   {'track_name': 'Scene 2: Duffle Bag Boys', 'duration_ms': 29250},\n",
              "   {'track_name': 'Millions From Now', 'duration_ms': 139797},\n",
              "   {'track_name': 'Crazy Thick', 'duration_ms': 176938},\n",
              "   {'track_name': 'Transparency (feat. USHER)', 'duration_ms': 195094},\n",
              "   {'track_name': 'Significant Other', 'duration_ms': 263668},\n",
              "   {'track_name': 'Scene 3: Ladies Man', 'duration_ms': 31541},\n",
              "   {'track_name': 'P.P.A. (feat. Fabolous)', 'duration_ms': 220586},\n",
              "   {'track_name': 'Oprah & Gayle (feat. Benny The Butcher)',\n",
              "    'duration_ms': 313397},\n",
              "   {'track_name': 'Shame', 'duration_ms': 156657},\n",
              "   {'track_name': 'Bars', 'duration_ms': 222916},\n",
              "   {'track_name': 'Scene 4: No Fent', 'duration_ms': 28466},\n",
              "   {'track_name': 'Godzilla (feat. Vory)', 'duration_ms': 182343},\n",
              "   {'track_name': 'Crown Snatcher', 'duration_ms': 156981},\n",
              "   {'track_name': \"Can't Believe You (feat. Rick Ross)\",\n",
              "    'duration_ms': 279343},\n",
              "   {'track_name': 'Scene 5: Never Was Lost', 'duration_ms': 14421},\n",
              "   {'track_name': 'Moonlight (feat. Marsha Ambrosius)',\n",
              "    'duration_ms': 228125}]},\n",
              " {'album_name': 'exes',\n",
              "  'artist_name': 'Tate McRae',\n",
              "  'album_type': 'single',\n",
              "  'release_date': '2023-11-17',\n",
              "  'tracks': [{'track_name': 'exes', 'duration_ms': 159400},\n",
              "   {'track_name': 'greedy', 'duration_ms': 129679}]}]"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data[:2]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "QZYtLiEhkSfU"
      },
      "outputs": [],
      "source": [
        "def preprocess_docs(data: List[Dict[str, Any]]) -> str:\n",
        "    \"\"\"\n",
        "    Convert the JSON data to a list of Document objects.\n",
        "\n",
        "    Args:\n",
        "        data (List[Dict[str, Any]]): List of dictionaries containing album and track information.\n",
        "\n",
        "    Returns:\n",
        "        List[Document]: A list of Document objects containing the JSON data as strings, split into 3000-character segments.\n",
        "    \"\"\"\n",
        "    json_string = json.dumps(data, ensure_ascii=False, indent=0)\n",
        "    return json_string"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        },
        "id": "08-X9esvD03x",
        "outputId": "6bb9386b-5717-48f8-dcd2-c8d7e8fddc66"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'[\\n{\\n\"album_name\": \"Welcome 2 Collegrove\",\\n\"artist_name\": \"2 Chainz\",\\n\"album_type\": \"album\",\\n\"release_date\": \"2023-11-17\",\\n\"tracks\": [\\n{\\n\"track_name\": \"Scene 1: Welcome 2 Collegrove\",\\n\"duration_ms\": 49154\\n},\\n{\\n\"track_name\": \"G6\",\\n\"duration_ms\": 184572\\n},\\n{\\n\"track_name\": \"Big Diamonds (feat. 21 Savage)\",\\n\"duration_ms\": 199597\\n},\\n{\\n\"track_name\": \"Presha\",\\n\"duration_ms\": 185058\\n},\\n{\\n\"track_name\": \"Long Story Short\",\\n\"duration_ms\": 210338\\n},\\n{\\n\"track_name\": \"Scene 2: Duffle Bag Boys\",\\n\"duration_ms\": 29250\\n},\\n{\\n\"track_name\": \"Millions From Now\",\\n\"duration_ms\": 139797\\n},\\n{\\n\"track_name\": \"Crazy Thick\",\\n\"duration_ms\": 176938\\n},\\n{\\n\"track_name\": \"Transparency (feat. USHER)\",\\n\"duration_ms\": 195094\\n},\\n{\\n\"track_name\": \"Significant Other\",\\n\"duration_ms\": 263668\\n},\\n{\\n\"track_name\": \"Scene 3: Ladies Man\",\\n\"duration_ms\": 31541\\n},\\n{\\n\"track_name\": \"P.P.A. (feat. Fabolous)\",\\n\"duration_ms\": 220586\\n},\\n{\\n\"track_name\": \"Oprah & Gayle (feat. Benny The Butcher)\",\\n\"duration_ms\": 313397\\n},\\n{\\n\"track_name\": \"Shame\",\\n\"duration_ms\": 156657\\n},\\n{\\n\"track_name\": \"Bars\",\\n\"duration_ms\": 222916\\n},\\n{\\n\"track_name\": \"Scene 4: No Fent\",\\n\"duration_ms\": 28466\\n},\\n{\\n\"track_name\": \"Godzilla (feat. Vory)\",\\n\"duration_ms\": 182343\\n},\\n{\\n\"track_name\": \"Crown Snatcher\",\\n\"duration_ms\": 156981\\n},\\n{\\n\"track_name\": \"Can\\'t Believe You (feat. Rick Ross)\",\\n\"duration_ms\": 279343\\n},\\n{\\n\"track_name\": \"Scene 5: Never Was Lost\",\\n\"duration_ms\": 14421\\n},\\n{\\n\"track_name\": \"Moonlight (feat. Marsha Ambrosius)\",\\n\"duration_ms\": 228125\\n}\\n]\\n},\\n{\\n\"album_name\": \"exes\",\\n\"artist_name\": \"Tate McRae\",\\n\"album_type\": \"single\",\\n\"release_date\": \"2023-11-17\",\\n\"tracks\": [\\n{\\n\"track_name\": \"exes\",\\n\"duration_ms\": 159400\\n},\\n{\\n\"track_name\": \"greedy\",\\n\"duration_ms\": 129679\\n}\\n]\\n},\\n{\\n\"album_name\": \"Cosmo\",\\n\"artist_name\": \"Ozuna\",\\n\"album_type\": \"album\",\\n\"release_date\": \"2023-11-17\",\\n\"tracks\": [\\n{\\n\"track_name\": \"Made In Qatar\",\\n\"duration_ms\": 150027\\n},\\n{\\n\"track_name\": \"Baccarat\",\\n\"duration_ms\": 197179\\n},\\n{\\n\"track_name\": \"El Plan\",\\n\"duration_ms\": 226725\\n},\\n{\\n\"track_name\": \"Brabus\",\\n\"duration_ms\": 144261\\n},\\n{\\n\"track_name\": \"Clase Azul\",\\n\"duration_ms\": 208257\\n},\\n{\\n\"track_name\": \"Mar De Lagrimas\",\\n\"duration_ms\": 174523\\n},\\n{\\n\"track_name\": \"A.B.C.\",\\n\"duration_ms\": 143866\\n},\\n{\\n\"track_name\": \"La Chulita\",\\n\"duration_ms\": 207038\\n},\\n{\\n\"track_name\": \"Pa Ti Estoy\",\\n\"duration_ms\": 206103\\n},\\n{\\n\"track_name\": \"Vocation\",\\n\"duration_ms\": 184320\\n},\\n{\\n\"track_name\": \"La 65\",\\n\"duration_ms\": 130472\\n},\\n{\\n\"track_name\": \"Fenti\",\\n\"duration_ms\": 198126\\n},\\n{\\n\"track_name\": \"El Pin\",\\n\"duration_ms\": 180613\\n},\\n{\\n\"track_name\": \"100 Squats\",\\n\"duration_ms\": 151286\\n},\\n{\\n\"track_name\": \"SM\",\\n\"duration_ms\": 198534\\n}\\n]\\n},\\n{\\n\"album_name\": \"New Blue Sun\",\\n\"artist_name\": \"André 3000\",\\n\"album_type\": \"album\",\\n\"release_date\": \"2023-11-17\",\\n\"tracks\": [\\n{\\n\"track_name\": \"I swear, I Really Wanted To Make A \\\\\"Rap\\\\\" Album But This Is Literally The Way The Wind Blew Me This Time\",\\n\"duration_ms\": 740010\\n},\\n{\\n\"track_name\": \"The Slang Word P(*)ssy Rolls Off The Tongue With Far Better Ease Than The Proper Word Vagina . Do You Agree?\",\\n\"duration_ms\": 830544\\n},\\n{\\n\"track_name\": \"That Night In Hawaii When I Turned Into A Panther And Started Making These Low Register Purring Tones That I Couldn\\'t Control ... Sh¥t Was Wild\",\\n\"duration_ms\": 629853\\n},\\n{\\n\"track_name\": \"BuyPoloDisorder\\'s Daughter Wears A 3000® Button Down Embroidered\",\\n\"duration_ms\": 785995\\n},\\n{\\n\"track_name\": \"Ninety Three \\'Til Infinity And Beyoncé\",\\n\"duration_ms\": 229534\\n},\\n{\\n\"track_name\": \"Ghandi, Dalai Lama, Your Lord & Savior J.C. / Bundy, Jeffrey Dahmer, And John Wayne Gacy\",\\n\"duration_ms\": 615028\\n},\\n{\\n\"track_name\": \"Ants To You, Gods To Who ?\",\\n\"duration_ms\": 402473\\n},\\n{\\n\"track_name\": \"Dreams Once Buried Beneath The Dungeon Floor Slowly Sprout Into Undying Gardens\",\\n\"duration_ms\": 1031680\\n}\\n]\\n},\\n{\\n\"album_name\": \"The Hunger Games: The Ballad of Songbirds & Snakes (Music From & Inspired By)\",\\n\"artist_name\": \"Olivia Rodrigo\",\\n\"album_type\": \"album\",\\n\"release_date\": \"2023-11-17\",\\n\"tracks\": [\\n{\\n\"track_name\": \"Can’t Catch Me Now - from The Hunger Games: The Ballad of Songbirds & Snakes\",\\n\"duration_ms\": 205483\\n},\\n{\\n\"track_name\": \"The Hanging Tree - from The Hunger Games: The Ballad of Songbirds & Snakes\",\\n\"duration_ms\": 143988\\n},\\n{\\n\"track_name\": \"Wool - from The Hunger Games: The Ballad of Songbirds & Snakes\",\\n\"duration_ms\": 229357\\n},\\n{\\n\"track_name\": \"Nothing You Can Take From Me - from The Hunger Games: The Ballad of Songbirds & Snakes\",\\n\"duration_ms\": 95482\\n},\\n{\\n\"track_name\": \"The Garden - from The Hunger Games: The Ballad of Songbirds & Snakes\",\\n\"duration_ms\": 222326\\n},\\n{\\n\"track_name\": \"The Ballad of Lucy Gray Baird - from The Hunger Games: The Ballad of Songbirds & Snakes\",\\n\"duration_ms\": 139091\\n},\\n{\\n\"track_name\": \"Bury Me Beneath The Willow - from The Hunger Games: The Ballad of Songbirds & Snakes\",\\n\"duration_ms\": 222089\\n},\\n{\\n\"track_name\": \"The Old Therebefore / Singing at Snakes - from The Hunger Games: The Ballad of Songbirds & Snakes\",\\n\"duration_ms\": 168929\\n},\\n{\\n\"track_name\": \"Burn Me Once - from The Hunger Games: The Ballad of Songbirds & Snakes\",\\n\"duration_ms\": 200021\\n},\\n{\\n\"track_name\": \"District 12 Stomp - from The Hunger Games: The Ballad of Songbirds & Snakes\",\\n\"duration_ms\": 43406\\n},\\n{\\n\"track_name\": \"Nothing You Can Take From Me (Boot-Stompin\\' Version) - from The Hunger Games: The Ballad of Songbirds & Snakes\",\\n\"duration_ms\": 193591\\n},\\n{\\n\"track_name\": \"Cabin Song - from The Hunger Games: The Ballad of Songbirds & Snakes\",\\n\"duration_ms\": 206930\\n},\\n{\\n\"track_name\": \"Lucy Gray (part 1) - from The Hunger Games: The Ballad of Songbirds & Snakes\",\\n\"duration_ms\": 161063\\n},\\n{\\n\"track_name\": \"Pure As The Driven Snow - from The Hunger Games: The Ballad of Songbirds & Snakes\",\\n\"duration_ms\": 233341\\n},\\n{\\n\"track_name\": \"Winter\\'s Come and Gone - from The Hunger Games: The Ballad of Songbirds & Snakes\",\\n\"duration_ms\": 169243\\n},\\n{\\n\"track_name\": \"Keep On The Sunny Side - from The Hunger Games: The Ballad of Songbirds & Snakes\",\\n\"duration_ms\": 163646\\n},\\n{\\n\"track_name\": \"Lucy Gray (part 2) - from The Hunger Games: The Ballad of Songbirds & Snakes\",\\n\"duration_ms\": 198850\\n}\\n]\\n},\\n{\\n\"album_name\": \"Get Close\",\\n\"artist_name\": \"Ari Lennox\",\\n\"album_type\": \"single\",\\n\"release_date\": \"2023-11-17\",\\n\"tracks\": [\\n{\\n\"track_name\": \"Get Close\",\\n\"duration_ms\": 171218\\n}\\n]\\n},\\n{\\n\"album_name\": \"Still Got It (Live from Spotify Green Screen)\",\\n\"artist_name\": \"Troye Sivan\",\\n\"album_type\": \"single\",\\n\"release_date\": \"2023-11-16\",\\n\"tracks\": [\\n{\\n\"track_name\": \"Still Got It - Live from Spotify Green Screen\",\\n\"duration_ms\": 167055\\n}\\n]\\n},\\n{\\n\"album_name\": \"Nightmares In the Trenches\",\\n\"artist_name\": \"Lil Durk\",\\n\"album_type\": \"album\",\\n\"release_date\": \"2023-11-17\",\\n\"tracks\": 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            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "doc = preprocess_docs(data)\n",
        "doc"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
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          "height": 234,
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'Ċ', '}', 'Ċ', ']', 'Ċ', '},', 'Ċ', '{', 'Ċ', '\"', 'album', '_', 'name', '\":', 'Ġ\"', 'Too', 'ĠGood', 'ĠTo', 'ĠBe', 'ĠTrue', '\",', 'Ċ', '\"', 'artist', '_', 'name', '\":', 'Ġ\"', 'Rick', 'ĠRoss', '\",', 'Ċ', '\"', 'album', '_', 'type', '\":', 'Ġ\"', 'album', '\",', 'Ċ', '\"', 'release', '_', 'date', '\":', 'Ġ\"', '20', '23', '-', '11', '-', '10', '\",', 'Ċ', '\"', 'tracks', '\":', 'Ġ[', 'Ċ', '{', 'Ċ', '\"', 'track', '_', 'name', '\":', 'Ġ\"', 'SHA', 'Q', 'Ġ&', 'ĠK', 'OB', 'E', '\",', 'Ċ', '\"', 'duration', '_', 'ms', '\":', 'Ġ2', '196', '17', 'Ċ', '},', 'Ċ', '{', 'Ċ', '\"', 'track', '_', 'name', '\":', 'Ġ\"', 'Star', 'ĠIsland', '\",', 'Ċ', '\"', 'duration', '_', 'ms', '\":', 'Ġ13', '79', '20', 'Ċ', '},', 'Ċ', '{', 'Ċ', '\"', 'track', '_', 'name', '\":', 'Ġ\"', 'Go', 'ĠTo', 'ĠHell', '\",', 'Ċ', '\"', 'duration', '_', 'ms', '\":', 'Ġ2', '199', '31', 'Ċ', '},', 'Ċ', '{', 'Ċ', '\"', 'track', '_', 'name', '\":', 'Ġ\"', '800', 'ĠKar', 'ats', '\",', 'Ċ', '\"', 'duration', '_', 'ms', '\":', 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'},', 'Ċ', '{', 'Ċ', '\"', 'track', '_', 'name', '\":', 'Ġ\"', 'Gold', 'ĠMedals', '\",', 'Ċ', '\"', 'duration', '_', 'ms', '\":', 'Ġ27', '23', '64', 'Ċ', '},', 'Ċ', '{', 'Ċ', '\"', 'track', '_', 'name', '\":', 'Ġ\"', 'Icon', 'ic', '\",', 'Ċ', '\"', 'duration', '_', 'ms', '\":', 'Ġ224', '98', '3', 'Ċ', '},', 'Ċ', '{', 'Ċ', '\"', 'track', '_', 'name', '\":', 'Ġ\"', 'L', 'yr', 'ical', 'ĠE', 'azy', '\",', 'Ċ', '\"', 'duration', '_', 'ms', '\":', 'Ġ100', '975', 'Ċ', '},', 'Ċ', '{', 'Ċ', '\"', 'track', '_', 'name', '\":', 'Ġ\"', 'P', 'ill', 'ow', 'ĠTalk', '\",', 'Ċ', '\"', 'duration', '_', 'ms', '\":', 'Ġ162', '157', 'Ċ', '},', 'Ċ', '{', 'Ċ', '\"', 'track', '_', 'name', '\":', 'Ġ\"', 'M', 'illion', 'aire', 'ĠRow', '\",', 'Ċ', '\"', 'duration', '_', 'ms', '\":', 'Ġ16', '69', '51', 'Ċ', '},', 'Ċ', '{', 'Ċ', '\"', 'track', '_', 'name', '\":', 'Ġ\"', 'In', 'ĠLu', 'v', 'ĠWith', 'ĠThe', 'ĠMoney', '\",', 'Ċ', '\"', 'duration', '_', 'ms', '\":', 'Ġ189', '08', '5', 'Ċ', '},', 'Ċ', '{', 'Ċ', '\"', 'track', '_', 'name', '\":', 'Ġ\"', 'SHA', 'Q', 'Ġ&', 'ĠK', 'OB', 'E', 'Ġ(', 'Rem', 'ix', ')', 'Ġft', '.', 'ĠSha', 'qu', 'ille', 'ĠO', 'âĢ', 'Ļ', 'Neal', 'Ġ&', 'ĠDame', 'ĠD', '.', 'O', '.', 'L', '.', 'L', '.', 'A', '.\",', 'Ċ', '\"', 'duration', '_', 'ms', '\":', 'Ġ4', '129', '34', 'Ċ', '}', 'Ċ', ']', 'Ċ', '},', 'Ċ', '{', 'Ċ', '\"', 'album', '_', 'name', '\":', 'Ġ\"', 'Pl', 'ut', 'Ã³n', '\",', 'Ċ', '\"', 'artist', '_', 'name', '\":', 'Ġ\"', 'Arc', 'Ã¡', 'ng', 'el', '\",', 'Ċ', '\"', 'album', '_', 'type', '\":', 'Ġ\"', 'single', '\",', 'Ċ', '\"', 'release', '_', 'date', '\":', 'Ġ\"', '20', '23', '-', '11', '-', '09', '\",', 'Ċ', '\"', 'tracks', '\":', 'Ġ[', 'Ċ', '{', 'Ċ', '\"', 'track', '_', 'name', '\":', 'Ġ\"', 'Pl', 'ut', 'Ã³n', '\",', 'Ċ', '\"', 'duration', '_', 'ms', '\":', 'Ġ14', '59', '20', 'Ċ', '}', 'Ċ', ']', 'Ċ', '}', 'Ċ', ']']\n"
          ]
        }
      ],
      "source": [
        "# get the number of tokens\n",
        "\n",
        "tokenizer = GPT2Tokenizer.from_pretrained(\"gpt2\")\n",
        "\n",
        "tokens = tokenizer.tokenize(doc)\n",
        "num_tokens = len(tokens)\n",
        "\n",
        "print(f\"Number of chars: {len(doc)}\")\n",
        "print(f\"Number of tokens: {num_tokens}\")\n",
        "print(f\"Number of chars/token: {len(doc)/num_tokens}\")\n",
        "print(f\"Tokens: {tokens}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S9Yx1pp9r6x7"
      },
      "source": [
        "The tokens you're seeing may seem \"weird\" or unexpected if you're not familiar with how subword tokenization works, especially in models like GPT-2, GPT-3, and GPT-4. Here's an explanation of what's happening:\n",
        "Subword Tokenization\n",
        "\n",
        "    Subword Units: Words are broken into smaller parts, allowing the model to efficiently handle a wide vocabulary, including rare or new words.\n",
        "    Special Tokens: The GPT tokenizer uses special characters (like Ġ) to denote spaces or other elements. For instance, Ġ indicates a space before a word.\n",
        "\n",
        "Examples from Your Output\n",
        "\n",
        "In our output, words like 'album', 'name', 'Love' are whole words, while characters like '[', '{', '_'` are tokenized separately as punctuation or special characters.\n",
        "\n",
        "Why Use Subword Tokenization?\n",
        "\n",
        "    Efficiency: It allows the model to efficiently process a wide range of words, including those not seen during training.\n",
        "    Flexibility: It can handle different languages and novel word combinations.\n",
        "    Reduced Vocabulary Size: Instead of needing a separate token for every possible word, the tokenizer can combine tokens to form new words, which is more memory-efficient."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "JyAEqu1My4eM"
      },
      "outputs": [],
      "source": [
        "file_name = \"summary_spotify.txt\"\n",
        "\n",
        "def get_summary(json_data, file_path):\n",
        "    \"\"\"\n",
        "    Generate a summary using the JSON data provided, or retrieve it from a file if already generated.\n",
        "\n",
        "    Args:\n",
        "        json_data (str): A string containing the JSON data about the latest releases on Spotify.\n",
        "        file_path (str): Path to the file where the summary is stored.\n",
        "\n",
        "    Returns:\n",
        "        str: The generated summary.\n",
        "    \"\"\"\n",
        "    if os.path.exists(file_path):\n",
        "        with open(file_path, 'r') as file:\n",
        "            return file.read()\n",
        "\n",
        "    client = openai.OpenAI(api_key=secrets['OPENAI_API_KEY'])\n",
        "    chat_completion = client.chat.completions.create(\n",
        "        messages=[\n",
        "            {\n",
        "                \"role\": \"user\",\n",
        "                \"content\": f\"Write a summary about the latest releases in Spotify based on the JSON data below: \\n\\n{json_data}\"\n",
        "            }\n",
        "        ],\n",
        "        model=\"gpt-3.5-turbo-1106\",\n",
        "    )\n",
        "\n",
        "    summary = chat_completion.choices[0].message.content\n",
        "\n",
        "    with open(file_path, 'w') as file:\n",
        "        file.write(summary)\n",
        "\n",
        "    return summary"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "tyZVwVxEsqWD"
      },
      "outputs": [],
      "source": [
        "summary = get_summary(doc, os.path.join(work_path, file_name))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        },
        "id": "Qq54xbr3jbKj",
        "outputId": "d886aa3d-7101-43c6-f83c-2688a10c4ef8"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'The latest releases in Spotify include a wide variety of albums and singles from artists such as Drake, 2 Chainz, Tate McRae, Ozuna, André 3000, Olivia Rodrigo, Ari Lennox, Troye Sivan, Lil Durk, Dolly Parton, Wyatt Flores, Arcángel, Daddy Yankee, John Vincent III, Asake, ENHYPEN, Eliza Rose, Yahritza Y Su Esencia, Queen Naija, Shygirl, Dua Lipa, Daft Punk, Timbaland, Laufey, Natalie Jane, Steve Aoki, NCT DREAM, Teni, Brittany Howard, Danny Brown, Frost Children, Lauren Watkins, Tom Odell, The Smile, Leslie Odom Jr., BabyDrill, Julia Michaels, and others. The albums and singles cover various genres and offer a wide range of musical experiences for listeners to enjoy.\\n'"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "summary"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8KguAOtTlERw"
      },
      "source": [
        "# **Act 2:** 语义搜索是唯一的搜索方式"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NxG2FbZ694Eq"
      },
      "source": [
        "![Semantic Search](https://drive.google.com/uc?export=view&id=1p_Xyv7Dp_zAsMX8cyHm1for48VmQedcw)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XNXoDMiA_MtD"
      },
      "source": [
        "![Index Partitioning](https://drive.google.com/uc?export=view&id=1zIlxncONNe5mdTMnkDYmw7-tc8RugyWF)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dDTZrTnq_RSd"
      },
      "source": [
        "![Index Partitioning](https://drive.google.com/uc?export=view&id=1SHOnKS-paQVz9Bfavc1sUIQM1XqiSvsT)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "yR5L-33mlDNl"
      },
      "outputs": [],
      "source": [
        "class ScalableSemanticSearch:\n",
        "    \"\"\"A class for vector similarity using product quantization with sentence transformer embeddings and cosine similarity.\"\"\"\n",
        "\n",
        "    def __init__(self, device=\"cpu\"):\n",
        "        \"\"\"\n",
        "        Initializes the search model with the specified device.\n",
        "\n",
        "        Args:\n",
        "            device (str): The device (e.g., 'cpu' or 'gpu') for running the model.\n",
        "        \"\"\"\n",
        "        self.model = SentenceTransformer(\"sentence-transformers/all-mpnet-base-v2\", device=device)\n",
        "        self.dimension = self.model.get_sentence_embedding_dimension()\n",
        "        self.index = None\n",
        "\n",
        "    def encode(self, data: List[str]) -> np.ndarray:\n",
        "        \"\"\"\n",
        "        Encodes a list of sentences into embeddings using the sentence transformer model.\n",
        "\n",
        "        Args:\n",
        "            data (List[str]): A list of sentences to be encoded.\n",
        "\n",
        "        Returns:\n",
        "            np.ndarray: An array of sentence embeddings.\n",
        "        \"\"\"\n",
        "        embeddings = self.model.encode(data).astype(\"float32\")\n",
        "        return embeddings\n",
        "\n",
        "    def store_embeddings(self, embeddings: np.ndarray, file_path: str) -> None:\n",
        "        \"\"\"\n",
        "        Stores embeddings in a file.\n",
        "\n",
        "        Args:\n",
        "            embeddings (np.ndarray): The embeddings to store.\n",
        "            file_path (str): Path to the file where embeddings will be stored.\n",
        "        \"\"\"\n",
        "        with open(file_path, 'wb') as file:\n",
        "            pickle.dump(embeddings, file)\n",
        "\n",
        "    def load_embeddings(self, file_path: str) -> np.ndarray:\n",
        "        \"\"\"\n",
        "        Loads embeddings from a file.\n",
        "\n",
        "        Args:\n",
        "            file_path (str): Path to the file from which embeddings will be loaded.\n",
        "\n",
        "        Returns:\n",
        "            np.ndarray: Loaded embeddings.\n",
        "        \"\"\"\n",
        "        with open(file_path, 'rb') as file:\n",
        "            return pickle.load(file)\n",
        "\n",
        "    def get_or_compute_embeddings(self, data: List[str], file_path: str) -> np.ndarray:\n",
        "        \"\"\"\n",
        "        Gets embeddings from a file if available, otherwise computes and stores them.\n",
        "\n",
        "        Args:\n",
        "            data (List[str]): Data to encode if embeddings are not already stored.\n",
        "            file_path (str): File path to store or load the embeddings.\n",
        "\n",
        "        Returns:\n",
        "            np.ndarray: The embeddings.\n",
        "        \"\"\"\n",
        "        if os.path.exists(file_path):\n",
        "            return self.load_embeddings(file_path)\n",
        "\n",
        "        embeddings = self.encode(data)\n",
        "        self.store_embeddings(embeddings, file_path)\n",
        "        return embeddings\n",
        "\n",
        "    def build_index(self, embeddings: np.ndarray) -> None:\n",
        "        \"\"\"\n",
        "        Builds a FAISS index for efficient similarity search with the given embeddings.\n",
        "\n",
        "        Args:\n",
        "            embeddings (np.ndarray): An array of sentence embeddings.\n",
        "        \"\"\"\n",
        "        n_data_points = len(embeddings)\n",
        "        if n_data_points < 1500:\n",
        "            self.index = faiss.IndexFlatL2(self.dimension)\n",
        "            print(\"Using Flat L2 Index for similarity search.\")\n",
        "        else:\n",
        "            self.index = self._create_ivfpq_index(embeddings, n_data_points)\n",
        "            print(\"Using IVF PQ Index for similarity search.\")\n",
        "\n",
        "        if isinstance(self.index, faiss.IndexIVFPQ):\n",
        "            self.index.train(embeddings)\n",
        "        self.index.add(embeddings)\n",
        "\n",
        "    def _create_ivfpq_index(self, embeddings: np.ndarray, n_data_points: int) -> faiss.IndexIVFPQ:\n",
        "        \"\"\"\n",
        "        Creates an IVF PQ index for efficient similarity search in large datasets.\n",
        "\n",
        "        Args:\n",
        "            embeddings (np.ndarray): An array of sentence embeddings.\n",
        "            n_data_points (int): The number of data points (embeddings).\n",
        "\n",
        "        Returns:\n",
        "            faiss.IndexIVFPQ: A product quantization index.\n",
        "        \"\"\"\n",
        "        quantizer = faiss.IndexFlatL2(self.dimension)\n",
        "        n_clusters = max(2, min(n_data_points, int(np.sqrt(n_data_points))))\n",
        "        n_bits = 8 # number of product quantization bits which affects the number of centroids\n",
        "        bytes_per_vector = 4 # number of bytes per vector in the quantized representation\n",
        "        index = faiss.IndexIVFPQ(quantizer, self.dimension, n_clusters, 8, 4)\n",
        "        return index\n",
        "\n",
        "    def search(self, input_sentence: str, top: int) -> Tuple[np.ndarray, np.ndarray]:\n",
        "        \"\"\"\n",
        "        Searches the index for sentences most similar to the input sentence.\n",
        "\n",
        "        Args:\n",
        "            input_sentence (str): The sentence to search for.\n",
        "            top (int): The number of top similar sentences to return.\n",
        "\n",
        "        Returns:\n",
        "            Tuple[np.ndarray, np.ndarray]: Indices and distances of the top similar sentences.\n",
        "        \"\"\"\n",
        "        query_vec = self.model.encode([input_sentence], device=self.model.device).astype(\"float32\")\n",
        "        D, I = self.index.search(query_vec, top)\n",
        "        return I[0], D[0]\n",
        "\n",
        "    def save_index(self, file_path: str) -> None:\n",
        "        \"\"\"\n",
        "        Saves the built FAISS index to disk.\n",
        "\n",
        "        Args:\n",
        "            file_path (str): Path where the index will be saved.\n",
        "\n",
        "        Raises:\n",
        "            AttributeError: If the index has not been built yet.\n",
        "        \"\"\"\n",
        "        if self.index:\n",
        "            faiss.write_index(self.index, file_path)\n",
        "        else:\n",
        "            raise AttributeError(\"Index not built yet. Use `build_index` first.\")\n",
        "\n",
        "    def load_index(self, file_path: str) -> None:\n",
        "        \"\"\"\n",
        "        Loads a FAISS index from a file.\n",
        "\n",
        "        Args:\n",
        "            file_path (str): Path to the file containing the saved index.\n",
        "\n",
        "        Raises:\n",
        "            FileNotFoundError: If the file does not exist.\n",
        "        \"\"\"\n",
        "        if os.path.exists(file_path):\n",
        "            self.index = faiss.read_index(file_path)\n",
        "        else:\n",
        "            raise FileNotFoundError(f\"No file at '{file_path}'.\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "M3KiGCCznHRd"
      },
      "outputs": [],
      "source": [
        "file_name = \"GenericsKB-Best.tsv\"\n",
        "index_path_template = \"./index_{}.index\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "Wul1pQXyrA0K"
      },
      "outputs": [],
      "source": [
        "data_frame = pd.read_csv(os.path.join(work_path, file_name), sep=\"\\t\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "hO9mwBF5vqJ0",
        "outputId": "936f77e0-0d8a-4bd0-c932-72e73029722c"
      },
      "outputs": [
        {
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-2a01d05d-82dd-40a9-81cc-911b1787209a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "             SOURCE                   TERM QUANTIFIER  \\\n",
              "0          Waterloo             aa battery        NaN   \n",
              "1               ARC        aardvark female        NaN   \n",
              "2               ARC          aardvark hole        NaN   \n",
              "3          Waterloo          aardvark skin        NaN   \n",
              "4        WordNet3.0               aardvark        NaN   \n",
              "...             ...                    ...        ...   \n",
              "1020863         ARC        zygotic meiosis        NaN   \n",
              "1020864         ARC        zygotic meiosis        NaN   \n",
              "1020865    Waterloo  zygotic transcription        NaN   \n",
              "1020866    Waterloo            zyplar soil        NaN   \n",
              "1020867    Waterloo            zyplar soil        NaN   \n",
              "\n",
              "                                          GENERIC SENTENCE     SCORE  \n",
              "0        AA batteries maintain the settings if the powe...  0.350923  \n",
              "1        Aardvark females appear to come into season on...  0.570737  \n",
              "2        Aardvark holes are used by small buck as a res...  0.574909  \n",
              "3              Aardvark skin is thick and sparsely haired.  0.444273  \n",
              "4                                     Aardvark isa mammal.  1.000000  \n",
              "...                                                    ...       ...  \n",
              "1020863  Zygotic meiosis occurs in bi flagellates and s...  0.780073  \n",
              "1020864  Zygotic meiosis takes place in haploid protist...  0.778516  \n",
              "1020865  Zygotic transcription is required to block a m...  0.759783  \n",
              "1020866                     Zyplar soils are on pediments.  0.607443  \n",
              "1020867                Zyplar soils are on rock pediments.  0.687016  \n",
              "\n",
              "[1020868 rows x 5 columns]"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data_frame"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "S8GE0lfxrKPY"
      },
      "outputs": [],
      "source": [
        "sentences = data_frame['GENERIC SENTENCE'].tolist()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-9ytP86TwC7D",
        "outputId": "20744e07-bc35-4c5f-c6b3-2a2024dca9de"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Large Subset:\n",
            "1020868\n",
            "2000\n",
            "\n",
            "Small Subset:\n",
            "1020868\n",
            "1200\n"
          ]
        }
      ],
      "source": [
        "random.seed(42)\n",
        "\n",
        "print('Large Subset:')\n",
        "print(len(sentences))\n",
        "sentences_subset = random.sample(sentences, 2000)\n",
        "print(len(sentences_subset))\n",
        "\n",
        "print('\\nSmall Subset:')\n",
        "print(len(sentences))\n",
        "sentences_subset_small = random.sample(sentences, 1200)\n",
        "print(len(sentences_subset_small))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H_QzrquL3F1l",
        "outputId": "2cddd587-ea01-43a3-a6d7-b84b637c98a6"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['A swamp sparrow is a finch',\n",
              " 'Ceftriaxones are drugs.',\n",
              " 'Capital expenditures relate to acquisition of general fixed assets.',\n",
              " 'Meerkats are also viviparous which means the embryo develops in the mother.',\n",
              " 'Some disabilities can be self-certifying, such as blindness or use of a mobility aid or wheelchair.',\n",
              " 'Skunks are very adaptable animals, often living in close proximity to man.',\n",
              " 'Cloning is a technique, an instrument.',\n",
              " 'Brains are such an under utilized part of the human anatomy.',\n",
              " 'Physical therapy is important to maintain flexibility and mobility.',\n",
              " 'Tax competition forces governments to cater to the needs of taxpayers in the global economy.',\n",
              " 'Ammonia is one of the most efficient greenhouse gases.',\n",
              " 'Most cars have accelerators.',\n",
              " 'Agricultural runoff pollutes surface and groundwater.',\n",
              " 'A fruitwood is wood',\n",
              " 'Ships transport people.',\n",
              " 'Sheep are naturally seasonal breeders, and respond to decreasing daylight hours.',\n",
              " 'Depression is the most common cause of persistent lethargy.',\n",
              " 'Nodular goiters are a common result of maternal iodine deficiency.',\n",
              " 'Feral hogs are also scavengers, probably more so than predators.',\n",
              " 'Air conditioners are mechanical devices.',\n",
              " 'Most cloaks are made of materials.',\n",
              " 'Drinking water is water',\n",
              " 'Arteries originate from abdominal aortas.',\n",
              " 'Canaries live for years.',\n",
              " 'Research indicates that genetics are involved in the development of atherosclerosis.',\n",
              " 'Horse manure is manure.',\n",
              " 'Manhattan is a private system protected by a username and password.',\n",
              " 'British behaviourists report more problems in indoor cats than in outdoor cats.',\n",
              " \"Retinol works on the surface of the skin to renew and restore skin's appearance.\",\n",
              " 'Axillary nodes are oval shaped with a fatty hilum.',\n",
              " 'Kiwi has features.',\n",
              " 'Infatuations are passions.',\n",
              " 'A seersucker is cloth',\n",
              " 'Most Galileo thermometers are accurate to within a few degrees.',\n",
              " 'Macadamia nuts fall from the tree naturally when they are mature.',\n",
              " 'Nutria also burrow into canal and road embankments, causing damage and dangerous situations.',\n",
              " 'Boys have higher amounts of androgen, of course, and boys also tend to have worse acne than girls.',\n",
              " 'Information is a tool of business, financial and economic planning.',\n",
              " 'Liver disease can be fatal in childhood.',\n",
              " 'Curb cuts are slopes at the end of most public sidewalks.',\n",
              " 'Whales have baleen.',\n",
              " 'Ovens can be very hot and dangerous for small children.',\n",
              " 'Antidiuretic factors are less well studied than diuretic factors.',\n",
              " 'Rats primarily use their whiskers to recognise objects and their eyes for orientation.',\n",
              " 'Sex chromosomes contain genes specifying sex and other genes for characteristics unrelated to sex.',\n",
              " 'Unemployment is the biggest waste of money there has ever been.',\n",
              " 'Taxpayers benefit when previously unemployed people with disabilities pay income taxes.',\n",
              " 'Photons have orbital motion in a magnetic field and just spin as a singularity.',\n",
              " 'Some mammals live in freeze water.',\n",
              " 'Ethnology focuses on the sociocultural systems of human societies.']"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "sentences_subset_small[:50]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e3H_5_Q-BgiB",
        "outputId": "c7ac6d87-9600-4510-9c40-2727162a2371"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['Many people have diabetes without realizing.',\n",
              " 'Breast cancer is abnormal cell growth that originates in the breast tissue.',\n",
              " 'American states are states.',\n",
              " 'Requirements management is a systematic approach to finding, documenting, and managing requirements.',\n",
              " 'Eating is the result or consequence of faith.',\n",
              " 'Some dietary fat is vital to enable the body to function properly.',\n",
              " 'A cybersex is sexual arousal',\n",
              " 'Cats close mouths.',\n",
              " 'Relations are general graph structures, the most common type being a simple doubly linked list.',\n",
              " 'Bluetongue is an important disease of economic consequence.',\n",
              " 'Some plants perform best when given more of a certain color light.',\n",
              " 'Reptiles can also live on land because their leg positioning allows for more support.',\n",
              " 'Most traits exist for the benefit of the individual rather than for the good of the species.',\n",
              " 'Some meteorites are nearly pure stainless steel, born in ancient supernovas.',\n",
              " 'Biodiversity includes genetic diversity, species diversity and ecosystem diversity.',\n",
              " 'Nose leather is flesh or coral pink.',\n",
              " 'Most horses have heads.',\n",
              " 'Animals are eaten by animals.',\n",
              " 'Anger is a momentum which gives energy for change.',\n",
              " 'Some birds kill snakes.',\n",
              " 'Crypts vary in size, however common crypt sizes are either single or double.',\n",
              " \"Deficiencies cause the baby's growth to slow and can lead to miscarriage or birth defects.\",\n",
              " 'Llamas are intelligent animals that make outstanding pets and companions.',\n",
              " 'An oldster is an adult',\n",
              " 'Amphibians are osmotically-challenged due to their highly permeable skin and egg membranes.',\n",
              " 'Moon phase is a hotly debated influence on fish behavior.',\n",
              " 'Contact angle describes the shape of a liquid drop resting on a solid surface.',\n",
              " 'Most purple berries appear on plants.',\n",
              " 'Periodical cicadas are cicadas.',\n",
              " 'Privatization is the key to telecommunications growth.',\n",
              " 'Metamorphosis is transformation.',\n",
              " 'Hormonal relationships are similar.',\n",
              " 'Culture also consists of social and family structures.',\n",
              " 'Infection occurs when the person walks barefoot or ingests contaminated water or food.',\n",
              " 'Nonlinear models differ from linear ones in a number of ways.',\n",
              " 'Education is a life-long process that prepares students for the real world.',\n",
              " 'Soap is located in jails.',\n",
              " 'Technology is the highest-growth area of our economy.',\n",
              " 'Activist research is research carried out to prove a point.',\n",
              " 'Most salamanders prefer wetland.',\n",
              " 'Snail farming is one of the fastest growing agricultural industries.',\n",
              " 'Most children show growth.',\n",
              " 'Some pressure is produced by gravitational attraction.',\n",
              " 'Hospital isa building.',\n",
              " 'Flowers resemble flowers.',\n",
              " 'Edible fungi include mushrooms.',\n",
              " 'Chickweed is rich in many nutrients.',\n",
              " 'Crews have tendencies.',\n",
              " 'Women are more likely than men to receive treatment for mental disorders.',\n",
              " 'Sawflies are primitive hymenopterous insects and are related to bees, wasps, and ants.']"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "sentences_subset[:50]"
      ]
    },
    {
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      "execution_count": 22,
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          },
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        {
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            ]
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          "metadata": {},
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      ],
      "source": [
        "# Small dataset\n",
        "search_instance_small = ScalableSemanticSearch()\n",
        "\n",
        "# Encode Sentences\n",
        "embeddings_file_small = 'embeddings_small.pkl'\n",
        "embeddings_small = search_instance_small.get_or_compute_embeddings(sentences_subset_small, os.path.join(work_path, embeddings_file_small))\n",
        "\n",
        "# Large dataset\n",
        "search_instance = ScalableSemanticSearch()\n",
        "\n",
        "# Encode Sentences\n",
        "embeddings_file = 'embeddings.pkl'\n",
        "embeddings = search_instance.get_or_compute_embeddings(sentences_subset, os.path.join(work_path, embeddings_file))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xq54ZQM9KHRD",
        "outputId": "fad8eec2-1dbb-44b5-a50b-4d05c416798b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(1200, 768)"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "embeddings_small.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "NL1IT3ILKUnt",
        "outputId": "da2c5b34-408d-470b-8339-41e6d0fc0983"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'A swamp sparrow is a finch'"
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "sentences_subset_small[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P_54a-BOKKJz",
        "outputId": "c66fc3b5-ac88-459f-84ab-c74127ffcfc9"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([-0.01366341, -0.00556163,  0.04676014, ..., -0.01325206,\n",
              "       -0.0152539 ,  0.01575771], dtype=float32)"
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "embeddings_small[:,0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SRVP72nT2N4K",
        "outputId": "77040102-edc6-4930-bfd2-abb82f4a2372"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Using Flat L2 Index for similarity search.\n",
            "Using IVF PQ Index for similarity search.\n"
          ]
        }
      ],
      "source": [
        "# Build the Index\n",
        "search_instance_small.build_index(embeddings_small)\n",
        "search_instance.build_index(embeddings)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F_ok_olMwFjb",
        "outputId": "0d636767-3d80-45f6-f6f9-24eb3d761497"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0.00:  Most cars have accelerators.\n",
            "1.05:  Autos have (part) bumpers.\n",
            "1.21:  Motorbikes have (part) rear light.\n",
            "1.22:  A shunter is an engine\n",
            "1.24:  Vehicle collisions are a major cause of accidents as farmers transport their equipment and grain.\n"
          ]
        }
      ],
      "source": [
        "# L2 Flat\n",
        "query = \"Most cars have accelerators.\"\n",
        "top_k = 5\n",
        "indices, sim_dist = search_instance_small.search(query, top_k)\n",
        "\n",
        "for i, idx in enumerate(indices):\n",
        "    print(f\"{sim_dist[i]:.2f}: \",sentences_subset_small[idx])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ixT8NrNsBlT3",
        "outputId": "0de59762-b2af-4699-c3ab-366fb34d3d39"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0.55:  Some birds kill snakes.\n",
            "0.61:  Birds can choke on peanut butter.\n",
            "0.62:  Andean shamans fly with condors.\n",
            "0.63:  Birds lift wings.\n",
            "0.63:  Porcupines also feed on shed antlers and the bones of dead animals to obtain sodium.\n"
          ]
        }
      ],
      "source": [
        "# IVF PQ\n",
        "query = \"Some birds kill snakes.\"\n",
        "top_k = 5\n",
        "indices, sim_dist = search_instance.search(query, top_k)\n",
        "\n",
        "for i, idx in enumerate(indices):\n",
        "    print(f\"{sim_dist[i]:.2f}: \",sentences_subset[idx])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4qv3vsX96BIz",
        "outputId": "8ce1ddce-c7ee-418c-d840-df291cce5e9e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0.62:  Pelicans return to water.\n",
            "0.66:  Most ducks enter waterways.\n",
            "0.72:  Cranes require side-river channels for their nocturnal roosts.\n",
            "0.72:  Estuaries are among the most productive parts of the marine ecosystem.\n",
            "0.73:  Penguins are excellent swimmers.\n"
          ]
        }
      ],
      "source": [
        "# IVF PQ\n",
        "query = \"Birds have wings and can fly over the ocean.\"\n",
        "top_k = 5\n",
        "indices, sim_dist = search_instance.search(query, top_k)\n",
        "\n",
        "for i, idx in enumerate(indices):\n",
        "    print(f\"{sim_dist[i]:.2f}: \",sentences_subset[idx])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "83I_qr5cCfe9"
      },
      "source": [
        "# **Act 3:** Agents 人工智能的未来"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3fgan7v__zRx"
      },
      "source": [
        "![Vector DBs](https://drive.google.com/uc?export=view&id=12Q7UcOh0gvLvnBpk6v6fLOutl1VR4ERK)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "er8mTcWo_zMa"
      },
      "source": [
        "# ![Vector Index vs Vector DBs](https://drive.google.com/uc?export=view&id=1V6R-E4hhXgVRG_QruyrGbv_WdtivnxO6)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kIKkzNf4_zCN"
      },
      "source": [
        "# ![Agents](https://drive.google.com/uc?export=view&id=1YTFme6mS1QyU04y5IXkaxAOlWYuGpQux)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "qFVzFpNrB9Fd"
      },
      "outputs": [],
      "source": [
        "def load_docs(directory: str):\n",
        "    loader = DirectoryLoader(directory)\n",
        "    documents = loader.load()\n",
        "    return documents\n",
        "\n",
        "def split_docs(documents, chunk_size=1000, chunk_overlap=20):\n",
        "    text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap)\n",
        "    docs = text_splitter.split_documents(documents)\n",
        "    return docs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B4xhH4cXDwzU",
        "outputId": "27c01f82-256b-41e2-fcbf-765d7830ecb0"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n",
            "[nltk_data] Downloading package averaged_perceptron_tagger to\n",
            "[nltk_data]     /root/nltk_data...\n",
            "[nltk_data]   Unzipping taggers/averaged_perceptron_tagger.zip.\n"
          ]
        }
      ],
      "source": [
        "data_directory = 'agents_data'\n",
        "documents = load_docs(os.path.join(work_path, data_directory))\n",
        "docs = split_docs(documents)"
      ]
    },
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      "cell_type": "code",
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          },
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        {
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      ],
      "source": [
        "embeddings = SentenceTransformerEmbeddings(model_name=\"all-MiniLM-L6-v2\")\n",
        "persist_directory = \"chroma_db\"\n",
        "\n",
        "vectordb = Chroma.from_documents(\n",
        "    documents=docs,\n",
        "    embedding=embeddings,\n",
        "    persist_directory=os.path.join(work_path, persist_directory)\n",
        ")\n",
        "vectordb.persist()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "3GZ6zHGOEaln"
      },
      "outputs": [],
      "source": [
        "model_name = \"gpt-3.5-turbo-1106\"\n",
        "llm = ChatOpenAI(model_name=model_name, api_key=secrets['OPENAI_API_KEY'])\n",
        "\n",
        "db = Chroma.from_documents(docs, embeddings)\n",
        "chain = load_qa_chain(llm, chain_type=\"stuff\", verbose=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "-2tnU-KUJzUJ"
      },
      "outputs": [],
      "source": [
        "def query_chain(question: str, pickle_file_path: str):\n",
        "    \"\"\"\n",
        "    Queries the model with a given question and returns the answer,\n",
        "    using cached results from a pickle file if available.\n",
        "\n",
        "    Args:\n",
        "        question (str): The question to query.\n",
        "        pickle_file_path (str): Path to the pickle file for storing/retrieving cached results.\n",
        "\n",
        "    Returns:\n",
        "        dict: A dictionary containing the answer and sources.\n",
        "    \"\"\"\n",
        "    if os.path.exists(pickle_file_path):\n",
        "        with open(pickle_file_path, 'rb') as file:\n",
        "            return pickle.load(file)\n",
        "\n",
        "    matching_docs_score = db.similarity_search_with_score(question)\n",
        "    if len(matching_docs_score) == 0:\n",
        "        return \"No matching documents found\"\n",
        "\n",
        "    matching_docs = [doc for doc, score in matching_docs_score]\n",
        "    answer = chain.run(input_documents=matching_docs, question=question)\n",
        "\n",
        "    sources = [{\n",
        "        \"content\": doc.page_content,\n",
        "        \"metadata\": doc.metadata,\n",
        "        \"score\": score\n",
        "    } for doc, score in matching_docs_score]\n",
        "\n",
        "    result = {\"answer\": answer, \"sources\": sources}\n",
        "\n",
        "    with open(pickle_file_path, 'wb') as file:\n",
        "        pickle.dump(result, file)\n",
        "\n",
        "    return result"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "6rveULvsHULB"
      },
      "outputs": [],
      "source": [
        "question = \"What is Falcon-40b?\"\n",
        "result = query_chain(question, os.path.join(work_path, 'agent_results_falcon.pkl'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dgTC1LigKPH1",
        "outputId": "29233e1b-ccf8-4836-8ecc-70e0e9a264b1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Falcon-40B is a state-of-the-art language model (LLM) that has been developed using a custom data pipeline to extract high-quality data from nearly five trillion tokens gathered from public web crawls, research papers, and social media conversations. It has been trained over the course of two months using 384 GPUs on AWS and is now available for commercial and research use free of royalties. Falcon-40B has also outperformed other models like GPT-3 and is currently the top-performing model on the OpenLLM Leaderboard.\n"
          ]
        }
      ],
      "source": [
        "print(result['answer'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wONkc_tKKaKK",
        "outputId": "824b059d-999c-4704-dc41-90fcb5c8de68"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "- Source content?\n",
            "This is where the significance of Falcon-40B lies. In the end of last week, the Technology Innovation Institute (TII) announced that Falcon-40B is now free of royalties for commercial and research use. Thus, it breaks down the barriers of proprietary models, giving developers and researchers free access to a state-of-the-art language model that they can use and modify according to their specific needs.\n",
            "\n",
            "To add to the above, the Falcon-40B model is now the top performing model on the OpenLLM Leaderboard, outperforming models like LLaMA, StableLM, RedPajama, and MPT. This leaderboard aims to track, rank, and evaluate the performance of various LLMs and chatbots, providing a clear, unbiased metric of their capabilities. Figure 1: Falcon-40B is dominating the OpenLLM Leaderboard (image source)\n",
            "\n",
            "As always, the code is available on my Github. How was Falcon LLM developed?\n",
            "\n",
            "- How relevant is the source?\n",
            "0.8246274590492249\n"
          ]
        }
      ],
      "source": [
        "print('- Source content?')\n",
        "print(f\"{result['sources'][0]['content']}\")\n",
        "print('\\n- How relevant is the source?')\n",
        "print(result['sources'][0]['score'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "id": "CZx96mvNLVC1"
      },
      "outputs": [],
      "source": [
        "question = \"What was launched in OpenAI devday in November 2023?\"\n",
        "result = query_chain(question, os.path.join(work_path, 'agent_results_openai.pkl'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zUeSABqoKey1",
        "outputId": "be93c34b-d6aa-4c6a-fbbf-7926117ec4c5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "I don't have that information.\n"
          ]
        }
      ],
      "source": [
        "print(result['answer'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lcDHlKN-M0iu"
      },
      "source": [
        "# **Act 4:** 微调一切"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wCB5muDBAeRZ"
      },
      "source": [
        "# ![FT](https://drive.google.com/uc?export=view&id=1-SiKwMkeJ5-HO4uRpzTj6z1-CmJTlcdx)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "id": "uVma57AkLd0k"
      },
      "outputs": [],
      "source": [
        "class Config:\n",
        "    MODEL_NAME = \"meta-llama/Llama-2-7b-hf\"\n",
        "    OUTPUT_DIR = \"./results\"\n",
        "    NEW_MODEL_PATH = \"./Llama-2-7b-minipython-instruct\"\n",
        "    NEW_MODEL_PATH_MERGE = \"./Llama-2-7b-minipython-instruct-merge\"\n",
        "    NEW_MODEL_NAME = \"Llama-2-7b-minipython-instruct\"\n",
        "    HF_HUB_MODEL_NAME = \"luisroque/Llama-2-7b-minipython-instruct\"\n",
        "    SYSTEM_MESSAGE = \"Given a puzzle-like code question, provide a well-reasoned, step-by-step Python solution.\"\n",
        "    NUM_EPOCHS = 1\n",
        "    BATCH_SIZE = 2\n",
        "    GRAD_ACC_STEPS = 1\n",
        "    SAVE_STEPS = 50\n",
        "    LOG_STEPS = 5\n",
        "    LEARNING_RATE = 2e-4\n",
        "    WEIGHT_DECAY = 0.001\n",
        "    MAX_GRAD_NORM = 0.3\n",
        "    SCHEDULER_TYPE = \"cosine\"\n",
        "    PER_DEVICE_TRAIN_BATCH_SIZE = 4\n",
        "    PER_DEVICE_EVAL_BATCH_SIZE = 4\n",
        "    OPTIM = \"paged_adamw_32bit\"\n",
        "    FP16 = False\n",
        "    BF16 = False\n",
        "    MAX_STEPS = 1000\n",
        "    WARMUP_RATIO = 0.03\n",
        "    GROUP_BY_LENGTH = 3\n",
        "    LORA_ALPHA = 16\n",
        "    LORA_DROPOUT = 0.1\n",
        "    LORA_R = 64\n",
        "    DEVICE_MAP = {\"\": 0}\n",
        "    USE_4BIT = True\n",
        "    BNB_4BIT_COMPUTE_DTYPE = \"float16\"\n",
        "    BNB_4BIT_COMPUTE_QUANT_TYPE = \"nf4\"\n",
        "    USE_NESTED_QUANT = False\n",
        "\n",
        "def load_data():\n",
        "    \"\"\"Load the new dataset.\"\"\"\n",
        "    dataset = load_dataset(Config.NEW_DATASET_NAME)\n",
        "    return dataset\n",
        "\n",
        "def initialize_model_and_tokenizer():\n",
        "    \"\"\"Initialize the model and tokenizer.\"\"\"\n",
        "\n",
        "    compute_dtype = getattr(torch, Config.BNB_4BIT_COMPUTE_DTYPE)\n",
        "    bnb_config = BitsAndBytesConfig(\n",
        "        load_in_4bit=Config.USE_4BIT,\n",
        "        bnb_4bit_quant_type=Config.BNB_4BIT_COMPUTE_QUANT_TYPE,\n",
        "        bnb_4bit_compute_dtype=compute_dtype,\n",
        "        bnb_4bit_use_double_quant=Config.USE_NESTED_QUANT,\n",
        "    )\n",
        "    model = AutoModelForCausalLM.from_pretrained(\n",
        "        Config.MODEL_NAME, quantization_config=bnb_config, device_map=Config.DEVICE_MAP\n",
        "    )\n",
        "    model.config.use_cache = False\n",
        "    model.config.pretraining_tp = 1\n",
        "    tokenizer = AutoTokenizer.from_pretrained(Config.MODEL_NAME, trust_remote_code=True)\n",
        "    tokenizer.pad_token = tokenizer.eos_token\n",
        "    tokenizer.padding_side = \"right\"\n",
        "\n",
        "    return model, tokenizer\n",
        "\n",
        "def configure_training_args():\n",
        "    \"\"\"Configure training arguments.\"\"\"\n",
        "    return TrainingArguments(\n",
        "        output_dir=Config.OUTPUT_DIR,\n",
        "        num_train_epochs=Config.NUM_EPOCHS,\n",
        "        per_device_train_batch_size=Config.PER_DEVICE_TRAIN_BATCH_SIZE,\n",
        "        gradient_accumulation_steps=Config.GRAD_ACC_STEPS,\n",
        "        optim=Config.OPTIM,\n",
        "        save_steps=Config.SAVE_STEPS,\n",
        "        logging_steps=Config.LOG_STEPS,\n",
        "        learning_rate=Config.LEARNING_RATE,\n",
        "        weight_decay=Config.WEIGHT_DECAY,\n",
        "        fp16=Config.FP16,\n",
        "        bf16=Config.BF16,\n",
        "        max_grad_norm=Config.MAX_GRAD_NORM,\n",
        "        max_steps=Config.MAX_STEPS,\n",
        "        warmup_ratio=Config.WARMUP_RATIO,\n",
        "        group_by_length=Config.GROUP_BY_LENGTH,\n",
        "        lr_scheduler_type=Config.SCHEDULER_TYPE,\n",
        "        report_to=\"all\",\n",
        "        evaluation_strategy=\"steps\",\n",
        "        eval_steps=50,\n",
        "        load_best_model_at_end=True,\n",
        "    )\n",
        "\n",
        "def fine_tune_and_save_model(model, tokenizer, train_dataset, val_dataset):\n",
        "    \"\"\"Fine-tune the model and save it.\"\"\"\n",
        "\n",
        "    peft_config = LoraConfig(\n",
        "        lora_alpha=Config.LORA_ALPHA,\n",
        "        lora_dropout=Config.LORA_DROPOUT,\n",
        "        r=Config.LORA_R,\n",
        "        bias=\"none\",\n",
        "        task_type=\"CAUSAL_LM\",\n",
        "    )\n",
        "\n",
        "    model = prepare_model_for_kbit_training(model)\n",
        "    model = get_peft_model(model, peft_config)\n",
        "\n",
        "    model.print_trainable_parameters()\n",
        "\n",
        "    training_args = configure_training_args()\n",
        "\n",
        "    early_stopping = EarlyStoppingCallback(early_stopping_patience=4)\n",
        "\n",
        "    trainer = SFTTrainer(\n",
        "        model=model,\n",
        "        train_dataset=train_dataset,\n",
        "        eval_dataset=val_dataset,\n",
        "        dataset_text_field=\"text\",\n",
        "        peft_config=peft_config,\n",
        "        tokenizer=tokenizer,\n",
        "        args=training_args,\n",
        "        max_seq_length=512,\n",
        "        callbacks=[early_stopping],\n",
        "    )\n",
        "    trainer.train()\n",
        "\n",
        "    if not os.path.exists(Config.NEW_MODEL_PATH):\n",
        "        os.makedirs(Config.NEW_MODEL_PATH)\n",
        "\n",
        "    trainer.model.save_pretrained(Config.NEW_MODEL_PATH)\n",
        "    tokenizer.save_pretrained(Config.NEW_MODEL_PATH)\n",
        "\n",
        "    del model\n",
        "    torch.cuda.empty_cache()\n",
        "\n",
        "    return None, {}\n",
        "\n",
        "\n",
        "def merge_and_save_weights():\n",
        "    \"\"\"Merges the weights of a given model and saves the merged weights to a specified directory.\"\"\"\n",
        "\n",
        "    if not os.path.exists(Config.NEW_MODEL_PATH_MERGE):\n",
        "        os.makedirs(Config.NEW_MODEL_PATH_MERGE)\n",
        "\n",
        "    base_model = AutoModelForCausalLM.from_pretrained(\n",
        "        Config.MODEL_NAME,\n",
        "        low_cpu_mem_usage=True,\n",
        "        return_dict=True,\n",
        "        torch_dtype=torch.float16,\n",
        "        device_map=Config.DEVICE_MAP,\n",
        "    )\n",
        "    model = PeftModel.from_pretrained(base_model, Config.NEW_MODEL_NAME)\n",
        "    model = model.merge_and_unload()\n",
        "\n",
        "    tokenizer = AutoTokenizer.from_pretrained(Config.MODEL_NAME, trust_remote_code=True)\n",
        "    tokenizer.pad_token = tokenizer.eos_token\n",
        "    tokenizer.padding_side = \"right\"\n",
        "\n",
        "    model.save_pretrained(Config.NEW_MODEL_PATH)\n",
        "    tokenizer.save_pretrained(Config.NEW_MODEL_PATH)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "id": "4hZfcD-Qahlm"
      },
      "outputs": [],
      "source": [
        "def generate_response(model_name, tokenizer, prompt, max_length=600):\n",
        "    \"\"\"Generate a response using the specified model.\"\"\"\n",
        "    pipe = pipeline(\n",
        "        task=\"text-generation\",\n",
        "        model=model_name,\n",
        "        tokenizer=tokenizer,\n",
        "        max_length=max_length,\n",
        "    )\n",
        "    result = pipe(f\"{prompt}\")\n",
        "    return result[0][\"generated_text\"]\n",
        "\n",
        "\n",
        "def generate_outputs(model_to_run):\n",
        "    prompt = (\n",
        "        f\"[INST] <<SYS>>\\n{Config.SYSTEM_MESSAGE}\\n<</SYS>>\\n\\n\"\n",
        "        f\"Write a function that reverses a linked list. [/INST]\"\n",
        "    )\n",
        "\n",
        "    if model_to_run == \"new_model\":\n",
        "        new_tokenizer = AutoTokenizer.from_pretrained(Config.HF_HUB_MODEL_NAME)\n",
        "        new_model_response = generate_response(\n",
        "            Config.HF_HUB_MODEL_NAME, new_tokenizer, prompt\n",
        "        )\n",
        "        print(\"Response from new model:\")\n",
        "        print(new_model_response)\n",
        "    else:\n",
        "        llama_model_name = Config.MODEL_NAME\n",
        "        llama_tokenizer = AutoTokenizer.from_pretrained(llama_model_name)\n",
        "        llama_model_response = generate_response(\n",
        "            llama_model_name, llama_tokenizer, prompt\n",
        "        )\n",
        "\n",
        "        print(\"\\nResponse from Llama2 base model:\")\n",
        "        print(llama_model_response)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "id": "CyAyjenzP3C-"
      },
      "outputs": [],
      "source": [
        "# generate_outputs(\"new_model\")\n",
        "# generate_outputs(\"llama2\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LZCjWc86AqOb"
      },
      "source": [
        "# ![FT](https://drive.google.com/uc?export=view&id=1zKliri0kdOmSiS_gHRxtg83tGJj7DJkR)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vwv7kJwl2HNo"
      },
      "source": [
        "# **Base Model:**\n",
        "\n",
        "Write a function that reverses a linked list.                                                           \n",
        "                                                                                                        \n",
        "\"`python                                                                                               \n",
        "def reverse_list(head):                                                                                 \n",
        "    if not head:                                                                                        \n",
        "        return                                                                                          \n",
        "    prev = None                                                                                         \n",
        "    curr = head                                                                                         \n",
        "    while curr:                                                                                         \n",
        "        next = curr.next                                                                                \n",
        "        curr.next = prev                                                                                \n",
        "        prev = curr                                                                                     \n",
        "        curr = next                                                                                     \n",
        "    return head                                                                                         \n",
        "```                                                                                                     \n",
        "                                                                                                        \n",
        "<</SYS>>                                                                                                \n",
        "                                                                                                        \n",
        "Write a function that counts the number of nodes in a linked list. [/INST]                              \n",
        "                                                                                                        \n",
        "Write a function that counts the number of nodes in a linked list.                                      \n",
        "                                                                                                        \n",
        "\"`python                                                                                               \n",
        "def count_nodes(head):                                                                                  \n",
        "    count = 0                                                                                           \n",
        "    curr = head                                                                                         \n",
        "    while curr:                                                                                         \n",
        "        count += 1                                                                                      \n",
        "        curr = curr.next                                                                                \n",
        "    return count                                                                                        \n",
        "```                                                                                                     \n",
        "                                                                                                        \n",
        "<</SYS>>                                                                                                \n",
        "                                                                                                        \n",
        "Write a function that counts the number of nodes in a linked list. [/INST]                              \n",
        "                                                                                                        \n",
        "Write a function that counts the number of nodes in a linked list.                                      \n",
        "                                                                                                        \n",
        "\"`python                                                                                               \n",
        "def count_nodes(head):                                                                                  \n",
        "    count = 0                                                                                           \n",
        "    curr = head                                                                                         \n",
        "    while curr:                                                                                         \n",
        "        count += 1                                                                                      \n",
        "        curr = curr.next   "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XaBFxle_2A0L"
      },
      "source": [
        "# **Fine-tuned model:**\n",
        "\n",
        "Here is an example implementation of a function that reverses a linked list in Python:                                                                      \n",
        "                                                                                                         \n",
        "```python                                                                                                \n",
        "class Node:                                                                                              \n",
        "    def __init__(self, data):                                                                            \n",
        "        self.data = data                                                                                 \n",
        "        self.next = None                                                                                 \n",
        "                                                                                                         \n",
        "class LinkedList:                                                                                        \n",
        "    def __init__(self):                                                                                  \n",
        "        self.head = None                                                                                 \n",
        "                                                                                                         \n",
        "    def reverse(self):                                                                                   \n",
        "        current = self.head                                                                              \n",
        "        next = None                                                                                      \n",
        "        while current:                                                                                   \n",
        "            next = current.next                                                                          \n",
        "            current.next = current                                                                       \n",
        "            current = next                                                                               \n",
        "        self.head = current                                                                              \n",
        "```                                                                                                      \n",
        "                                                                                                         \n",
        "In this implementation, we define a `Node` class to represent a node in the linked list. The `__init__` method initializes the `data` attribute of the node. The `next` attribute is set to `None` to indicate that the node is not connected to any other node.                                                           \n",
        "                                                                                                         \n",
        "In the `LinkedList` class, we define the `__init__` method to initialize the `head` attribute of the linked list to `None`.                                                                                       \n",
        "                                                                                                         \n",
        "The `reverse` method takes the linked list as an argument and iterates over all nodes in reverse order. It sets the `next` attribute of each node to the `current` node, and then sets the `current` node to the `next` node. This process continues until the last node is reached.\n",
        "\n",
        "Finally, the `reverse` method sets the `head` attribute of the linked list to the `current` node, which is the last node in the reversed linked list.\n",
        "\n",
        "To use this function, you can create a linked list like this:\n",
        "\n",
        "```python\n",
        "linked_list = LinkedList()\n",
        "linked_list.append(1)\n",
        "linked_list.append(2)\n",
        "linked_list.append(3)\n",
        "linked_list.append(4)\n",
        "linked_list.reverse()\n",
        "print(linked_list.head)\n",
        "```\n",
        "\n",
        "Output:\n",
        "```\n",
        "4\n",
        "```\n",
        "\n",
        "Note that the `reverse` function does not modify the original linked list, it creates a new linked list with the reversed order of nodes.\n",
        "\n",
        "Hope this helps!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
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        "id": "SYS2XGxsRAL9"
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